Downscaling Extremes for Fire Risk Assessment

• Activity Number: 32
• Type: Contributed
• Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Benjamin Shaby*
• Companies: Penn State
• Keywords: MAx-stable ; Spatial ; Hierarchical

Abstract

Wildfires have the potential to inflict huge losses of life, infrastructure, and habitat. High-resolution climate models can simulate weather variables that influence ignition probability and spread potential, but these models produce distributions for these variables that have unrealistic tails. We use spatial max-stable models to extract meaningful information about extreme fire weather in California from high-resolution weather model output by constructing a spatial downscaling model within the max-stable framework. This allows us to treat climate model output as covariates, rather than as future weather variables, in a spatial extreme value regression model fit to observed data.